External tufted (ET) cells are a class of interneuron within the glomerular layer of the olfactory bulb (OB) that recently have been shown to have multiple important roles in the coding and processing of olfactory sensory information. ET cells possess striking dynamical characteristics such as spontaneous bursting at respiratory theta frequency and exhibit highly correlated activity within a glomerulus. By providing feedforward excitation to other local interneurons and to principal output neurons, ET cells may amplify sensory input, contribute to noise suppression, and act as network pacemakers, synchronizing glomerular output and regulating the dynamics of deeper OB circuitry that determine the information exported from the OB to other regions of the brain. A thorough understanding of the functional role of ET cells in olfactory information processing is hindered, however, both by the complexity of synaptic interactions within and among glomeruli and by intricate and sometimes contradictory experimental data regarding the connectivity of OB circuitry. Here, I propose a computational modeling approach to identify the coordinated dynamical mechanisms of ET cells and assess their contributions to the encoding and processing of olfactory information - a task at which biophysically realistic computational modeling excels. Specifically, I propose a step-by-step construction of biophysical cellular and network models of the OB, based on and constrained by extensive published physiological data. Cellular models of ET cells and single-glomerular networks will be developed first, and vetted by experimental data. These models then will be integrated into a multiglomerular network model incorporating interglomerular interactions and deeper OB circuitry to investigate the dynamical role of ET cells in generating and coordinating global OB oscillatory dynamics. This systematic and quantitative integration of complex datasets into a single framework is essential for an integrated understanding of ET cell function, and will produce experimentally testable predictions.